You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

You agree to adhere to all terms and conditions for using the model as specified by the IEA License Agreement.

Log in or Sign Up to review the conditions and access this model content.

Model Card for ICILS XLM-R ISCO

This model is a fine-tuned version of ESCOXLM-R trained on The ICILS Multilingual ISCO-08 Parental Occupation Corpus.

A R&D report explaining the research is available at https://www.iea.nl/publications/rd-outcomes/improving-parental-occupation-coding-procedures-ai.

It achieves the following results on the test split:

  • Loss: 1.7849
  • Accuracy: 0.6285
  • Hierarchical Accuracy: 0.95

The research paper, ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain, states "ESCOXLM-R, based on XLM-R-large, uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations" (Zhang et al., ACL 2023).

Model Details

Model Description

IEA is an international cooperative of national research institutions, governmental research agencies, scholars, and analysts working to research, understand, and improve education worldwide.

Model Sources

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 12.0

Training results

Training Loss Epoch Step Accuracy Validation Loss
3.2269 1.0 3518 0.4176 2.9434
2.2851 2.0 7036 0.5250 2.2479
1.937 3.0 10554 0.5691 1.9822
1.4695 4.0 14072 0.6018 1.8560
1.2157 5.0 17590 0.6114 1.8160
0.9819 6.0 21108 0.6214 1.7946
0.8608 7.0 24626 0.6285 1.7849
0.8374 8.0 28144 0.6353 1.7893
0.7908 9.0 31662 1.8279 0.6239
0.6962 10.0 35180 1.8472 0.6347
0.6371 11.0 38698 1.8669 0.6339
0.5226 12.0 42216 1.8695 0.6336

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was trained on the icils configuration of the ISCO-08 dataset using the train and validation splits and evaluated on the test split.

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Downloads last month
4
Safetensors
Model size
560M params
Tensor type
F32
ยท
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for ICILS/xlm-r-icils-ilo

Finetuned
(1)
this model

Spaces using ICILS/xlm-r-icils-ilo 2

Evaluation results